Cloud Platform Engineer

About the job Build the cloud platform that puts Graphcore AI systems into real use. As a Senior Cloud Engineer, you will develop and operate private cloud services for Graphcore teams and users. You will turn product and user requirements into reliable, deployed services. Your work will support large-scale AI systems built from Graphcore hardware, high-performance servers, networks and storage. These systems help engineers validate, benchmark and optimise the platforms behind advanced AI compute. You will use Infrastructure-as-Code, observability and automation to deploy, test and improve cloud infrastructure. You will also work with datacentre, software and product teams to keep critical systems performing. This is hands-on engineering with real ownership. You will often work with pre-release hardware and software, where judgement and pace matter. The team and culture The Cloud Platform Team builds the systems that give internal users and customers access to Graphcore AI infrastructure. Work moves from requirements to deployed services, with engineers close to the users and systems they support. You will work across Software Platform, Datacentre Operations and Product Development. Decisions are shaped by technical evidence, user needs and the realities of operating critical infrastructure. This team suits engineers who take responsibility, speak up and move quickly. You will be trusted to work independently, solve hard platform problems and keep services available. What we’re looking for Bachelor's degree or equivalent practical experience in a relevant subject. Solid software engineering or IT experience, with proven delivery as an individual contributor. Experience managing on-premises or private-cloud environments. Strong Linux scripting and system administration skills across bash, Python, awk, sed, Ubuntu, RHEL and variants. Experience with Git, CI pipelines and Infrastructure-as-Code tools such as Terraform/OpenTofu, Ansible or Packer. Hands-on

About the job Build the cloud platform that powers Graphcore’s next generation AI systems. As a Principal Cloud Engineer, you will help develop, deploy and operate cloud services across Graphcore’s AI infrastructure. You will work on private and public cloud systems used by internal teams and customers. Your work will shape how high-performance AI systems are integrated, validated, benchmarked and optimised. You will help keep complex infrastructure reliable, observable and ready for demanding workloads. You will own significant technical initiatives, from OpenStack-based services to Infrastructure-as-Code automation. You will also mentor engineers and turn user needs into services that perform at scale. This role puts you close to pre-release hardware, advanced systems and real product challenges. The Team and Culture The Cloud Platform Team sits within Software Platform and works closely with Software Platform, Datacentre Operations and Product Development. The team builds systems for internal users and customers, often using new components before they are proven. Work moves through practical engineering, clear ownership and fast feedback from users. You will be expected to take responsibility, speak up early and lead critical infrastructure work without close oversight. Decisions are grounded in evidence from benchmarks, observability data and operational performance. The team values engineers who can think big, act fast and bring others with them. What we’re looking for Bachelor's degree or equivalent practical experience in a relevant subject. Strong infrastructure or IT background, with private-cloud or on-premises cloud experience. Expert Linux system administration and scripting skills, including bash and Python. Hands-on experience with Infrastructure-as-Code tools such as Terraform/OpenTofu, Ansible or Packer. Experience with CI or testing pipelines, version control and cloud service deployment. Strong communication skills, with the judgement

About the job Build the cloud platform that turns Graphcore AI systems into usable, scalable services. Graphcore needs a Bristol-based Staff Cloud Engineer to develop and deploy private and public cloud services for AI compute. You will join the Cloud Platform team within Software Platform, close to datacentre operations and product development. Your work will shape how internal users and customers access large-scale AI systems. You will keep critical infrastructure available while helping pre-release hardware and software become reliable cloud services. You will operate and extend OpenStack-based services, automate deployment with infrastructure-as-code, and improve observability across the fleet. You will benchmark, validate and optimise systems spanning Graphcore hardware, servers, switches and storage. This role suits someone who wants ownership of hard infrastructure problems, not a narrow ticket queue. Expect unproven components, direct user feedback, and decisions that move quickly into production. The team and culture The Cloud Platform team turns Graphcore products into large-scale AI solutions for internal users and customers. Work happens close to the systems, with engineers deploying, measuring and improving services directly. You will work with Software Platform, Datacentre Operations, Product Development, Engineering and QA. The team values people who take responsibility for unclear problems and make progress without waiting for perfect inputs. The team makes decisions from evidence: metrics, benchmarks, user impact and system behaviour. Engineers speak up, surface risks early and own corrective actions when systems need attention. What we’re looking for A relevant degree or equivalent practical experience, with proven ability to scope and deliver technical infrastructure work. Experience operating on-premises or private-cloud environments, with strong ownership of end-user availability. Strong Linux system administration and scripting sk

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

About the Role Sigma is growing rapidly, and our Technical Support Engineering team is scaling alongside it to meet the needs of an expanding global user base. As a Technical Support Engineer at Sigma, you will be part of an award-winning team recognized with the 2024 Stevie Gold Award for Customer Service, helping customers solve technical, business, and data challenges using the Sigma platform. You’ll work closely with Product, Engineering, and Go-to-Market teams to diagnose complex issues, drive solutions, and contribute to the continuous improvement of our product and support operations. What You Will Be Doing You will be working on diagnosing and resolving complex technical issues in real time via live chat, working directly with customers to troubleshoot. Periodically working as support on-call, handling high-severity incidents with urgency and end-to-end ownership. Working closely with the development team to develop best practices and tools for diagnosing issues and optimizing the service for performance. Participating in quarterly projects to improve automation, tooling, and processes focused on operational efficiency. Collaborating with cross-functional groups - backend, frontend, devops, design, product, and the go-to-market teams to create a first-class experience for users of our product. Beyond supporting customers, you also participate in product demos, bug bashes, knowledge shares, and building automation and internal tools for operational efficiency. Qualifications We Are Looking For Core Technical Skills 2+ years of experience in a customer-facing technical role (e.g., Technical Support, Solutions Engineer, or Software Engineer) at a cloud and/or SaaS provider. Advanced SQL proficiency and expertise at writing high-performance queries, including window functions, UDFs, and complex joins to manipulate large-scale datasets. Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure) and hands-on exp